Local Maximum and Minimum Surface Temperature

Figure. Annual maximum (red) and minimum (blue) temperature at Koror. The solid black line represents a trend that is statistically significant (p < 0.05). The dashed black line represents a trend that is not statistically significant.

Setup

First, we need to import all the necessary libraries. Some of them are specifically developed to handle the download and plotting of the data and are hosted at the indicators set-up repository in GitHub

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import warnings
warnings.filterwarnings("ignore")

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import os.path as op
import sys
import contextlib

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from myst_nb import glue 

sys.path.append("../../../../indicators_setup")
from ind_setup.plotting_int import plot_timeseries_interactive, fig_int_to_glue

sys.path.append("../../../functions")
from data_downloaders import GHCN, filter_by_time_completeness

Define location and variables of interest

country = 'Palau'
vars_interest = ['TMIN', 'TMAX']

Get Data

update_data = False
path_data = "../../../data"
path_figs = "../../../matrix_cc/figures"

Observations from Koror Station

https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/doc/GHCND_documentation.pdf

The data used for this analysis comes from the GHCN (Global Historical Climatology Network)-Daily database.
This a database that addresses the critical need for historical daily temperature, precipitation, and snow records over global land areas. GHCN-Daily is a composite of climate records from numerous sources that were merged and then subjected to a suite of quality assurance reviews. The archive includes over 40 meteorological elements including temperature daily maximum/minimum, temperature at observation time, precipitation and more.

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if update_data:
    df_country = GHCN.get_country_code(country)
    print(f'The GHCN code for {country} is {df_country["Code"].values[0]}')

    df_stations = GHCN.download_stations_info()
    df_country_stations = df_stations[df_stations['ID'].str.startswith(df_country.Code.values[0])]
    print(f'There are {df_country_stations.shape[0]} stations in {country}')

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if update_data:
    GHCND_dir = 'https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/access/'
    id = 'PSW00040309' # Koror Station
    dict_min = GHCN.extract_dict_data_var(GHCND_dir, 'TMIN', df_country_stations.loc[df_country_stations['ID'] == id])[0][0]
    dict_max = GHCN.extract_dict_data_var(GHCND_dir, 'TMAX', df_country_stations.loc[df_country_stations['ID'] == id])[0][0]
    st_data = pd.concat([dict_min['data'], (dict_max['data'])], axis=1).dropna()
    st_data['diff'] = st_data['TMAX'] - st_data['TMIN']
    st_data['TMEAN'] = (st_data['TMAX'] + st_data['TMIN'])/2
    st_data.to_pickle(op.join(path_data, 'GHCN_surface_temperature.pkl'))
else:
    st_data = pd.read_pickle(op.join(path_data, 'GHCN_surface_temperature.pkl'))

# Inclusion criteria: remove months/years with less than 75% of data

df_filt, removed_months, removed_years = filter_by_time_completeness(
    st_data,
    month_threshold=0.75,
    year_threshold=0.75
)
print(f"Removed {removed_months.shape[0]} months due to insufficient data: {removed_months.index.tolist()}")
print(f"Removed {removed_years.shape[0]} years due to insufficient data: {removed_years.index.tolist()}")

st_data = df_filt #replace data with filtered data
st_data_daily = st_data.loc[st_data.TMEAN < 50].copy()

Removed 14 months due to insufficient data: [(2018, 8), (2019, 1), (2020, 3), (2022, 7), (2022, 10), (2022, 11), (2022, 12), (2023, 1), (2023, 6), (2023, 8), (2023, 12), (2024, 2), (2025, 8), (2025, 10)]
Removed 3 years due to insufficient data: [2019, 2022, 2023]

print(st_data_daily.TMIN.mean(), st_data_daily.TMAX.mean())

24.383973736353393 31.058716344846346

dict_plot = [{'data' : st_data_daily, 'var' : 'TMAX', 'ax' : 1, 'label' : 'TMAX'}]
fig = plot_timeseries_interactive(dict_plot, trendline=True, figsize = (25, 12))

dict_plot = [{'data' : st_data_daily, 'var' : 'TMIN', 'ax' : 1, 'label' : 'TMIN'}]
fig = plot_timeseries_interactive(dict_plot, trendline=True, figsize = (25, 12))

st_data = st_data.resample('Y').mean()
glue("n_years", len(np.unique(st_data.index.year)), display=False)
glue("start_year", st_data.dropna().index[0].year, display=False)
glue("end_year", st_data.dropna().index[-1].year, display=False)

Plotting Annual

At this piece of code we will plot the Mean annual temperature over time and its associated trend

The following plot represent the average minimum and maximum temperature over time

Minimum Temperatures

dict_plot = [{'data' : st_data, 'var' : 'TMIN', 'ax' : 1, 'label' : 'TMIN'},
        ]

fig, trend_minimum = plot_timeseries_interactive(dict_plot, trendline=True, figsize = (24, 11), return_trend = True)
fig.write_html(op.join(path_figs, 'F3_ST_min.html'), include_plotlyjs="cdn")

Maximum Temperatures

dict_plot = [{'data' : st_data, 'var' : 'TMAX', 'ax' : 1, 'label' : 'TMAX'},
        ]

fig, trend_maximum = plot_timeseries_interactive(dict_plot, trendline=True, figsize = (24, 11), return_trend = True)
fig.write_html(op.join(path_figs, 'F3_ST_max.html'), include_plotlyjs="cdn")

print(st_data.TMIN.mean(), st_data.TMAX.mean())

24.412562826647335 31.045708280937074

Minimum and Maximum Temperatures

dict_plot = [{'data' : st_data, 'var' : 'TMIN', 'ax' : 1, 'label' : 'TMIN'},
        {'data' : st_data, 'var' : 'TMAX', 'ax' : 1, 'label' : 'TMAX'},
        # {'data' : st_data, 'var' : 'diff', 'ax' : 1, 'label' : 'Difference TMAX - TMIN'}
        ]

fig, TRENDS = plot_timeseries_interactive(dict_plot, label_yaxes = 'Temperature [ºC]', trendline=True, figsize = (24, 11), return_trend = True)
fig.write_html(op.join(path_figs, 'F3_ST_min_max.html'), include_plotlyjs="cdn")

glue("trend_min", float(TRENDS[0]), display=False)
glue("trend_max", float(TRENDS[1]), display=False)

glue("change_min", float(TRENDS[0]*len(np.unique(st_data.index.year))), display=False)
glue("change_max", float(TRENDS[1]*len(np.unique(st_data.index.year))), display=False)

glue("trend_fig_max_min", fig_int_to_glue(fig), display=False)

Fig. Annual maximum (red) and minimum (blue) temperature at Koror. The solid black line represents a trend that is statistically significant (p < 0.05). The dashed black line represents a trend that is not statistically significant.

Difference temperature

The following plot represents the avergae difference between the minimum and maximum temperature over time. The decreasing trend means that the daily variability is decreasing over time

dict_plot = [{'data' : st_data, 'var' : 'diff', 'ax' : 1, 'label' : 'Difference TMAX - TMIN'}]
fig, trend = plot_timeseries_interactive(dict_plot, trendline=True, figsize = (25, 12), return_trend = True)
glue("trend_diff", float(trend[0]), display=False)

Fig. Annual maximum of the difference of the maximum and minimum temperature within each day

Generate table

The final step is to generate a table sumarizing different metrics of the data analyzed in the plots above

from ind_setup.tables import table_temp_12, style_matrix

style_matrix(table_temp_12(st_data, trend_maximum[0], trend_minimum[0]))

Key Metrics Summary

Metric	Value
Annual Maximum Temperature (°C)	32.104
Change in Annual Maximum Temperature since 1951	0.296
Rate of Change in Annual Maximum Temperature (°C/year)	0.004
Annual Minimum Temperature (°C)	23.757
Change in Annual Minimum Temperature since 1951	0.962
Rate of Change in Annual Minimum Temperature (°C/year)	0.013